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Course Outline

Introduction to Generative AI and Agentic AI

  • Defining Generative AI and Agentic AI
  • Key differences and synergies between the two
  • Industry use cases and current trends

Generative AI Architecture and Tools

  • Transformer-based models: GPT, LLaMA, Claude, and others
  • Comparing fine-tuning with in-context learning
  • Essential tools: ChatGPT, Hugging Face Transformers, Google AI Studio

Prompt Engineering for Control and Structure

  • Prompt patterns for writing, coding, summarization, and more
  • Techniques such as few-shot, zero-shot, and chain-of-thought prompting
  • Leveraging prompt libraries and testing utilities

Understanding Agentic AI

  • Definition and evolution of agentic AI
  • Core architectures: planning, memory, tool usage, and self-reflection
  • Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph

Designing and Deploying Autonomous Agents

  • Establishing goals and decomposing tasks
  • Integrating tools and APIs (search, memory, code execution)
  • Coordinating multi-agent systems and incorporating human-in-the-loop supervision

Use Cases and Implementation Scenarios

  • Distinguishing between content generation and task orchestration
  • Applications in enterprise productivity, customer support, and data extraction
  • Ensuring responsible and secure implementation practices

Summary and Next Steps

Requirements

  • A foundational understanding of AI and machine learning principles
  • Experience using APIs or scripting languages such as Python
  • Familiarity with prompt engineering or the utilization of large language models

Target Audience

  • AI developers and engineers
  • Innovation and Research & Development (R&D) teams
  • Technical product managers interested in exploring agentic AI systems
 14 Hours

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